Unsupervised Learning Task

An Unsupervised Learning Task is a Learning Task where no Labeled Training Cases are provided.

• AKA: Unsupervised Learning, Unsupervised, Unsupervised Learning Problem.
• Context:
  • Input: Cases (Data Records).
  • Output: Model that Predicts a Test Case's Cluster.
  • It can be solved solved by an Unsupervised Learning System (that implements an Unsupervised Learning Algorithm) or manually by a human (e.g. visually).
• Example(s)
  • It can be a Clustering Task.
• See: Supervised Learning Task, Reinforcement Learning Task, Probability Density Estimation Task, Exploratory Data Analysis.

References

2009

• (Wikipedia, 2009) http://en.wikipedia.org/wiki/Unsupervised_learning
  • In machine learning, unsupervised learning is a class of problems in which one seeks to determine how the data are organized. It is distinguished from supervised learning (and reinforcement learning) in that the learner is given only unlabeled examples.
  • Unsupervised learning is closely related to the problem of density estimation in statistics. However unsupervised learning also encompasses many other techniques that seek to summarize and explain key features of the data.
  • One form of unsupervised learning is clustering. Another example is blind source separation based on Independent Component Analysis (ICA).

2000

• (Valpola, 2000) => Harri Valpola. (2000). "Bayesian Ensemble Learning for Nonlinear Factor Analysis. PhD Dissertation, Helsinki University of Technology.
  • Quote: unsupervise learning: The goal in unsupervised learning is to find an internal representation of the statistical structure of the observations. See supervised learning.

1998

• (Kohavi & Provost, 1998) => Ron Kohavi, and Foster Provost. (1998). "Glossary of Terms." In: Machine Leanring 30(2-3).
  • Unsupervised learning: Learning techniques that group instances without a pre-specified dependent attribute. Clustering algorithms are usually unsupervised.